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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Afriza, Dini Aprilia
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Biplot Analysis for Spatial Mapping of Dengue Hemorrhagic Fever (DHF) Incidence in Indonesia Gani, Fadjryani Abdul; Aisya, Cici; Ainanur; Afriza, Dini Aprilia
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 2 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i2.17401

Abstract

Dengue Hemorrhagic Fever (DHF) is a serious threat to Indonesian public health, with the dengue virus spread by the Aedes aegypti mosquito continuing to claim victims in all provinces in Indonesia. The drastic variation of DHF incidence between provinces requires an in-depth understanding of its distribution pattern. Biplot analysis allows researchers to identify patterns based on factors that influence the incidence of DHF in different provinces. This study aims to identify the spatial distribution pattern of DHF in Indonesia using biplot analysis, an approach that allows complex visualization of factors affecting DHF incidence. Results showed that 62.48% of the data variation could be explained through biplot representation, revealing spatial distribution patterns, proximity between objects and diversity between variables. Key findings include the identification of provinces with the highest DHF cases (56,388 cases) in quadrant IV, the high incidence of DHF cases was associated with similar characteristics of average air humidity. In addition, there was significant variation in the number of DHF cases between provinces indicating disparities in the number of DHF cases in different parts of Indonesia, as well as relative uniformity in the percentage of households with proper sanitation (descriptive average of 86.62%). The results of this study are expected to assist policy makers in formulating more effective and targeted dengue prevention and control strategies, potentially reducing the incidence of dengue and improving the health of the Indonesian people.
Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor Setiawan, Iman; Musa, Mohammad Dahlan Th.; Afriza, Dini Aprilia; Hafidah, Siti Nur
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8905

Abstract

Measuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371).
MODELING THE IDX30 STOCK INDEX USING STEP FUNCTION INTERVENTION ANALYSIS Rais, Rais; Afriza, Dini Aprilia; Setiawan, Iman; Sain, Hartayuni; Fadjryani, Fadjryani; Junaidi, Junaidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2057-2068

Abstract

The significant decline in the IDX30 stock index occurred due to an intervention, namely the COVID-19 pandemic, which affected market stability and investment decisions. This study aims to model and forecast the IDX30 stock index using intervention analysis with a step function, which is very suitable for capturing long-term external shocks. The methodology used includes the ARIMA (AutoRegressive Integrated Moving Average) model combined with step function intervention analysis to account for structural changes due to external disturbances. The data used is sourced from investing.com, consisting of weekly IDX30 stock index prices from January 2019 to December 2023. The results show that the COVID-19 pandemic significantly impacted the IDX30 index, causing a drastic decline. The best model identified is ARIMA (1,2,1) with intervention parameters b = 0, s = 0, and r = 1. The forecasting results range from Rp. 488 to Rp. 505, with a Mean Absolute Percentage Error (MAPE) of 1.9404%, which shows the forecasting results are very good, indicating high forecasting accuracy. These findings highlight the effectiveness of intervention analysis in modeling financial time series data affected by external disturbances.
ANALYSIS OF PRIORITY AREAS FOR HANDLING STUNTING CASES IN SIGI REGENCY USING THE TOPSIS METHOD BASED ON WEB DASHBOARD Mu'arif, Zainal; Afriza, Dini Aprilia; Aulia, Firda; Anggelina E, Melsy Patricia; Gamayanti, Nurul Fiskia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1411-1422

Abstract

Stunting is a condition of growth failure in children, where a toddler has a length or height below the average. Stunting is a problem for children because it has the potential to slow down brain development with prolonged effects. Central Sulawesi Province is one of the provinces with the highest stunting prevalence rate and the area with the highest stunting rate is in Sigi Regency at 36.8%. Stunting cases are an important concern for the Sigi Regency government, especially the Health Office and Community Health Centers. To identify and determine areas that are prioritized for handling stunting cases, seven indicators are used, including the number of stunting cases, number of villages covered, number of health workers, number of integrated service posts, number of exclusive breastfeeding, percentage of clean drinking water, and percentage of proper sanitation. To support in reducing the percentage of stunting in Sigi Regency, research was conducted and a web dashboard system application was made to support priority area selection decisions using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, a best alternative method that has the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The results obtained in this study are the areas that are prioritized for handling stunting cases in Sigi Regency is the Sigi Biromaru area with a total of 495 stunting cases, the number of coverage villages is 18, the number of integrated service posts is 53, the number of health workers is 96, the number of exclusive breastfeeding is 35, the percentage of proper drinking water is 44%, and the percentage of proper sanitation is 84.00% with the highest preference value through the TOPSIS method analysis of 0.660.
Co-Authors Ainanur Aisya, Cici Anggelina E, Melsy Patricia AULIA, FIRDA Fadjryani, Fadjryani Gamayanti, Nurul Fiskia Hafidah, Siti Nur Hartayuni Sain Iman Setiawan Junaidi Junaidi Mu'arif, Zainal Musa, Mohammad Dahlan Th. Rais Rais